1. Technical Field
This application relates to the field of data management systems. More specifically, the application relates to data management systems for managing data for financial institutions, such as credit unions.
2. Background Information
Managers of financial institutions may desire to quickly generate reports and gain insight from customer data to determine what types of transactions customers are conducting, how these transactions are trending, who their most profitable customers are within a given time period, and what products were purchased by these customers. They may want to understand the mix of their loan portfolio in terms of loan type and risk category and within a defined period of time. These and other insights may be advantageous in achieving business objectives, for banks and credit unions, for example, which typically have business objectives that include increasing loan originations, improving cash management, increasing marketing program yields, increasing productivity, or measuring compliance.
Traditional financial institution data management solutions are limited in the level of data insight and reporting capabilities they offer. Useful data may be available from various sources, including core applications and external data sources or applications. However, driving value from the data through integrated, comprehensive reporting and insight cannot be quickly accomplished since it usually requires a significant amount of manual effort, which may include manual extraction from applications and manually manipulating the data, using tools such as Microsoft Excel, into a desired form. There is thus a need for improved integration of data that supports more robust and comprehensive reporting and analytic capabilities.
Traditional systems also suffer from a lack of scalability. That is, financial institutions must often absorb significant costs of installing complex data management solutions upfront in order to have the data management capabilities required for future growth of their business. Moreover, any scalability provided in traditional systems is not usually tied to the value delivered to customers or members by the overall system. There is thus a need for a scalable data management system architecture, which allows financial institutions to increase capabilities in an economical manner tied to the value delivered to customers or members. There is an associated need for a data model, which facilitates such a scalable system architecture.